Clustering Adaptive Elephant Herd Optimization Based Data Dissemination Protocol for VANETs

In this paper, two meta-heuristic approaches have been optimized using SI (Swarm Intelligence) optimization technique. The method comprises computation of shortest path of both, static and dynamic IoT (Internet of Things) network using "widely used algorithms" among researchers, namely, 'Breadth first search' and 'Dijkstra algorithm.' Further, the determined route has been optimized using ant colony optimization. The issue of route selection to reach the destination, as well as parameters such as network energy, departed node count, residual energy of IoT nodes, and critical points of IoT nodes have been explored using proposed smart routing techniques. The two unique routing approaches have been simulated with rigorous iteration run, i=2000. The proposed methods, 'Ant colony optimization-Breadth first computation-Minkowski-Static' (ABMS) technique and 'Ant colony optimization-Breadth first computation-Minkowski-Dynamic' (ABMD) technique have been simulated. After comparing efficiency between both techniques, ABMS method outperforms the ABMD routing technique for IoT network. A significant energy savings has been reported, extending network lifetime of a static IoT network scenario. With the implementation of both techniques, the comparison between dynamic and static results closely.

“…Where, αbit is number of bits transmitted over a longdistance k. The path loss components are k 2 , k 4 . The transmitted power dissipated by Gelec.…”

Section: Energy Modelmentioning

confidence: 99%

“…The PSO (Particle Swarm Optimization) has an advantage of fast convergence. A Population-based optimization method contains a group of individuals, moving in multidimensional space 4) . Each particle possesses position and velocity at any instant of time.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization Based Routing Strategies for Internet of Things

Sharma¹,

Sharma²,

Gupta³

2023

“…It is more likely to be affected by flow, speed and density. To achieve an optimal solution, the system should be equipped with all elements like sensors and data analytics capability shown by Dwivedy et al 27) . For efficient traffic signal policy, the neural network, reinforcement learning techniques are suggested by Dief et al 25) include the following algorithms-Multi-agent system and reinforcement learning framework.…”

Section: Literature Reviewmentioning

confidence: 99%

A Real Time Dynamic Approach for Management of Vehicle Generated Traffic

Chaudhary¹,

Saraswat²,

Yadav³

et al. 2023

India, with a population of 1.3 billion and almost 300 million vehicles, is one of the biggest contributors to traffic jams, vehicle-specific pollution, and chronic lung diseases. To manage the footfall of these gigantic vehicles, continuous effort and research is taking place in the direction of both active and passive traffic management. This research paper aims towards showcasing a dynamic, fully autonomous deep learning model that uses real-time feeds from existing traffic junctions/ intersection cameras, process them and provide an intensity score based on the density of traffic in each adjoining lane.The proposed CNN model which is based on YOLO framework uses 10 seconds wait analysis time. The proposed system manages traffic, based on the intensity scores which assign traffic a Go time to each lane using an optimal traffic time in the range of 10-50 seconds.The model also scans for emergency vehicles in each lane, to provide a priority pass to such vehicles. Evaluation of model performance Mean Average Precision (mAP) is used.

“…One of the algorithms for cluster analysis is the k-means method. K-means is a straightforward and effective algorithm for finding clusters 21,22) . The algorithm for kmeans is as follows: The determination of the closest distance in Stage 3 usually uses the Euclidean distance.…”

Section: Cluster Analysismentioning

confidence: 99%

Evaluation of Efficiency in Logistics Company: An Analysis of Last-Mile Delivery

Surjandari¹,

Rindrasari²,

Dhini³

2023

Major developments in the e-commerce business have an impact on supporting industries, such as the logistics industry. Logistics service providers for e-commerce consumers must focus on the quality of their services. Service quality is determined by meeting customer expectations, including on-time delivery, parcel security, and customer service. In the process of sending parcels, one thing that affects the timeliness of delivery is the last-mile delivery stage. Due to a large number of last-mile stations and the geographic dispersion of their locations throughout Indonesia, the service quality between stations becomes inhomogeneous. The performance of the last-mile station can be measured by comparing the relative efficiency between stations. In this study, the initial stage of the efficiency analysis of the last-mile delivery station is by conducting a cluster analysis that divides the station into two groups, namely the Leader and Majority clusters, which aim to group homogeneous DMUs. Efficiency analysis with DEA is given for each cluster. The result shows that 94 of the 133 stations are relatively efficient for the Leader cluster stations, and 136 of the 466 stations are relatively efficient for the Majority cluster stations. A decision tree method is used for classification modeling to determine the characteristics of a relatively efficient station. Total daily delivery becomes the initial variable that determines the station to enter the Leader or Majority cluster. The second criterion is the variable number and quantity of goods delivered on time.